one noise variable, linear regression
## [1] "*************************************************************"
## [1] "one noise variable, linear regression"
## [1] "bSigmaBest 31"
## [1] "naive effects model"
## [1] "one noise variable, linear regression naive effects model fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2322 -0.6020 0.0120 0.5804 3.2574
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.001467 0.019623 0.075 0.94
## n1 1.000321 0.038697 25.850 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8776 on 1998 degrees of freedom
## Multiple R-squared: 0.2506, Adjusted R-squared: 0.2503
## F-statistic: 668.2 on 1 and 1998 DF, p-value: < 2.2e-16
##
## [1] " train rmse 0.87711349635425"
## [1] " application rmse 1.15239485807949"
## [1] "one noise variable, linear regression naive effects model train rmse 0.87711349635425"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.140]
## [1] "one noise variable, linear regression naive effects model test rmse 1.15239485807949"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.293]
## [1] "effects model, sigma= 31"
## [1] "one noise variable, linear regression effects model, sigma= 31 fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4220 -0.6769 -0.0023 0.6672 3.8897
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0014637 0.0226673 0.065 0.949
## n1 0.0005436 0.0018795 0.289 0.772
##
## Residual standard error: 1.014 on 1998 degrees of freedom
## Multiple R-squared: 4.187e-05, Adjusted R-squared: -0.0004586
## F-statistic: 0.08365 on 1 and 1998 DF, p-value: 0.7724
##
## [1] " train rmse 1.01320688574545"
## [1] " application rmse 0.995730393710376"
## [1] "one noise variable, linear regression Noised 31 train rmse 1.01320688574545"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.446]
## [1] "one noise variable, linear regression Noised 31 test rmse 0.995730393710376"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.599]
## [1] "effects model, jacknifed"
## [1] "one noise variable, linear regression effects model, jackknifed fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4251 -0.6776 -0.0009 0.6645 3.8913
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.001465 0.022668 0.065 0.948
## n1 0.004279 0.038189 0.112 0.911
##
## Residual standard error: 1.014 on 1998 degrees of freedom
## Multiple R-squared: 6.285e-06, Adjusted R-squared: -0.0004942
## F-statistic: 0.01256 on 1 and 1998 DF, p-value: 0.9108
##
## [1] " train rmse 1.01322491166252"
## [1] " application rmse 0.99567998170435"
## [1] "one noise variable, linear regression jackknifed train rmse 1.01322491166252"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.752]
## [1] "one noise variable, linear regression jackknifed test rmse 0.99567998170435"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.905]
## [1] "********"
## [1] "one noise variable, linear regression JackknifeModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9809 0.9974 1.0010 1.0010 1.0050 1.0230
## [1] 0.006856151
## [1] "********"
## [1] "********"
## [1] "one noise variable, linear regression NaiveModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.113 1.141 1.149 1.150 1.159 1.186
## [1] 0.01358256
## [1] "********"
## [1] "********"
## [1] "one noise variable, linear regression NoisedModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9812 0.9977 1.0010 1.0020 1.0060 1.0240
## [1] 0.007057484
## [1] "********"
## [1] "********"
## [1] "one noise variable, linear regression ObliviousModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9807 0.9968 1.0010 1.0010 1.0050 1.0230
## [1] 0.006906042
## [1] "********"
## [1] "*************************************************************"
one variable, linear regression
## [1] "*************************************************************"
## [1] "one variable, linear regression"
## [1] "bSigmaBest 5"
## [1] "naive effects model"
## [1] "one variable, linear regression naive effects model fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3721 -0.6891 -0.0037 0.6848 3.7826
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20623 0.02260 9.125 <2e-16 ***
## x1 1.00000 0.03685 27.137 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.011 on 1998 degrees of freedom
## Multiple R-squared: 0.2693, Adjusted R-squared: 0.269
## F-statistic: 736.4 on 1 and 1998 DF, p-value: < 2.2e-16
##
## [1] " train rmse 1.01025938596012"
## [1] " application rmse 0.999915402747535"
## [1] "one variable, linear regression naive effects model train rmse 1.01025938596012"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.1355]
## [1] "one variable, linear regression naive effects model test rmse 0.999915402747535"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.1508]
## [1] "effects model, sigma= 5"
## [1] "one variable, linear regression effects model, sigma= 5 fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3818 -0.6911 0.0001 0.6855 3.7908
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20623 0.02261 9.123 <2e-16 ***
## x1 0.99850 0.03682 27.115 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.011 on 1998 degrees of freedom
## Multiple R-squared: 0.269, Adjusted R-squared: 0.2686
## F-statistic: 735.2 on 1 and 1998 DF, p-value: < 2.2e-16
##
## [1] " train rmse 1.01047885565484"
## [1] " application rmse 1.00065689591244"
## [1] "one variable, linear regression Noised 5 train rmse 1.01047885565484"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.1661]
## [1] "one variable, linear regression Noised 5 test rmse 1.00065689591244"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.1814]
## [1] "effects model, jacknifed"
## [1] "one variable, linear regression effects model, jackknifed fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3933 -0.6946 -0.0039 0.6875 3.7985
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2062 0.0227 9.084 <2e-16 ***
## x1 0.9871 0.0370 26.682 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.015 on 1998 degrees of freedom
## Multiple R-squared: 0.2627, Adjusted R-squared: 0.2623
## F-statistic: 712 on 1 and 1998 DF, p-value: < 2.2e-16
##
## [1] " train rmse 1.01481235978284"
## [1] " application rmse 1.00008428967326"
## [1] "one variable, linear regression jackknifed train rmse 1.01481235978284"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.1967]
## [1] "one variable, linear regression jackknifed test rmse 1.00008428967326"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.2120]
## [1] "********"
## [1] "one variable, linear regression JackknifeModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9784 0.9975 1.0020 1.0020 1.0070 1.0200
## [1] 0.007216416
## [1] "********"
## [1] "********"
## [1] "one variable, linear regression NaiveModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9788 0.9975 1.0020 1.0020 1.0070 1.0200
## [1] 0.007184955
## [1] "********"
## [1] "********"
## [1] "one variable, linear regression NoisedModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9791 0.9980 1.0020 1.0030 1.0070 1.0200
## [1] 0.00733544
## [1] "********"
## [1] "********"
## [1] "one variable, linear regression ObliviousModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.141 1.169 1.174 1.173 1.178 1.191
## [1] 0.008053831
## [1] "********"
## [1] "*************************************************************"
one variable plus noise variable, linear regression
## [1] "*************************************************************"
## [1] "one variable plus noise variable, linear regression"
## [1] "bSigmaBest 16"
## [1] "naive effects model"
## [1] "one variable plus noise variable, linear regression naive effects model fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9216 -0.6181 0.0055 0.6225 3.5298
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20622 0.02058 10.02 <2e-16 ***
## x1 0.83459 0.03452 24.17 <2e-16 ***
## n1 0.78131 0.03844 20.33 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9203 on 1997 degrees of freedom
## Multiple R-squared: 0.3946, Adjusted R-squared: 0.394
## F-statistic: 650.8 on 2 and 1997 DF, p-value: < 2.2e-16
##
## [1] " train rmse 0.919591353886876"
## [1] " application rmse 1.12246743812363"
## [1] "one variable plus noise variable, linear regression naive effects model train rmse 0.919591353886876"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.2570]
## [1] "one variable plus noise variable, linear regression naive effects model test rmse 1.12246743812363"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.2723]
## [1] "effects model, sigma= 16"
## [1] "one variable plus noise variable, linear regression effects model, sigma= 16 fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4478 -0.6824 0.0031 0.6861 3.7488
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.206227 0.022652 9.104 <2e-16 ***
## x1 0.931336 0.034614 26.906 <2e-16 ***
## n1 0.004203 0.003263 1.288 0.198
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.013 on 1997 degrees of freedom
## Multiple R-squared: 0.2664, Adjusted R-squared: 0.2657
## F-statistic: 362.6 on 2 and 1997 DF, p-value: < 2.2e-16
##
## [1] " train rmse 1.01227540136427"
## [1] " application rmse 1.01240166375178"
## [1] "one variable plus noise variable, linear regression Noised 16 train rmse 1.01227540136427"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.2876]
## [1] "one variable plus noise variable, linear regression Noised 16 test rmse 1.01240166375178"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.3029]
## [1] "effects model, jacknifed"
## [1] "one variable plus noise variable, linear regression effects model, jackknifed fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3986 -0.6920 -0.0077 0.6877 3.8126
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20643 0.02268 9.101 <2e-16 ***
## x1 0.98425 0.03698 26.614 <2e-16 ***
## n1 -0.07739 0.03479 -2.224 0.0262 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.014 on 1997 degrees of freedom
## Multiple R-squared: 0.2645, Adjusted R-squared: 0.2638
## F-statistic: 359.2 on 2 and 1997 DF, p-value: < 2.2e-16
##
## [1] " train rmse 1.01355772650768"
## [1] " application rmse 1.00913108707443"
## [1] "one variable plus noise variable, linear regression jackknifed train rmse 1.01355772650768"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.3182]
## [1] "one variable plus noise variable, linear regression jackknifed test rmse 1.00913108707443"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.3335]
## [1] "********"
## [1] "one variable plus noise variable, linear regression JackknifeModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9879 0.9977 1.0030 1.0030 1.0080 1.0230
## [1] 0.007536811
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, linear regression NaiveModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.108 1.124 1.133 1.134 1.142 1.166
## [1] 0.01222749
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, linear regression NoisedModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.991 1.004 1.010 1.010 1.016 1.042
## [1] 0.008694371
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, linear regression ObliviousModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.152 1.166 1.174 1.173 1.180 1.197
## [1] 0.00938885
## [1] "********"
## [1] "*************************************************************"
one variable plus noise variable, diagonal regression
## [1] "*************************************************************"
## [1] "one variable plus noise variable, diagonal regression"
## [1] "bSigmaBest 11"
## [1] "naive effects model"
## [1] "one variable plus noise variable, diagonal regression naive effects model fit model:"
## x1 n1
## 1.000005 1.000333
## [1] " train rmse 0.958540237968956"
## [1] " application rmse 1.20618715828122"
## [1] "one variable plus noise variable, diagonal regression naive effects model train rmse 0.958540237968956"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.3785]
## [1] "one variable plus noise variable, diagonal regression naive effects model test rmse 1.20618715828122"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.3938]
## [1] "effects model, sigma= 11"
## [1] "one variable plus noise variable, diagonal regression effects model, sigma= 11 fit model:"
## x1 n1
## 0.954185969 0.009397325
## [1] " train rmse 1.03131856719671"
## [1] " application rmse 1.03377361327229"
## [1] "one variable plus noise variable, diagonal regression Noised 11 train rmse 1.03131856719671"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.4091]
## [1] "one variable plus noise variable, diagonal regression Noised 11 test rmse 1.03377361327229"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.4244]
## [1] "effects model, jacknifed"
## [1] "one variable plus noise variable, diagonal regression effects model, jackknifed fit model:"
## x1 n1
## 0.9871528 -0.1088369
## [1] " train rmse 1.03458802692346"
## [1] " application rmse 1.03176880530955"
## [1] "one variable plus noise variable, diagonal regression jackknifed train rmse 1.03458802692346"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.4397]
## [1] "one variable plus noise variable, diagonal regression jackknifed test rmse 1.03176880530955"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.4550]
## [1] "********"
## [1] "one variable plus noise variable, diagonal regression JackknifeModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.004 1.016 1.022 1.022 1.027 1.042
## [1] 0.008365958
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, diagonal regression NaiveModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.181 1.208 1.218 1.220 1.230 1.295
## [1] 0.01970578
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, diagonal regression NoisedModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.011 1.021 1.029 1.031 1.038 1.089
## [1] 0.01373885
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, diagonal regression ObliviousModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.150 1.165 1.171 1.172 1.178 1.190
## [1] 0.00864882
## [1] "********"
## [1] "*************************************************************"